1. Field of the Invention
The present invention relates to a method, system and apparatus for determining and modifying saliency of a visual medium. More specifically, the method, system and apparatus may obtain saliency values for a visual medium based on a plurality of visual channels. The saliency values may be obtained based on at least one of computer-generated modeling, user-specified input and eye-tracking. The method, system and apparatus may aggregate the obtained saliency values and classify regions of the visual medium based on the aggregated saliency values. The visual channels may include one or more of absolute mean curvature, a gradient of mean curvature, a gradient of color intensity, color luminance, color opponency, color saturation, lighting and focus. When using mean curvature, the method, system and apparatus may calculate a change in mean curvature for a plurality of vertices around a region and displace the vertices in accordance with the calculated change in mean curvature to change a saliency of the region.
2. Description of the Related Art
While visualization and simulation datasets have been growing at a rapid rate, the capabilities of human perceptual and cognitive systems have naturally remained unchanged. As a result, visualization datasets can often overwhelm the limits of human comprehension. To enable next-generation visual analysis, reasoning and discovery environments, new methodologies are desirable to develop effective visual presentation of important information that is well-grounded in principles of human perception. To address this challenge, it is important to understand the principles that elicit visual attention. Understanding these principles may be useful in allocating computational and rendering resources commensurate with human visual attention, enabling the design of superior algorithms to guide visual attention to regions and objects deemed to be important, and facilitating the development of novel visual abstraction, summarization and depiction methods for visual and simulation datasets by leveraging insights from the human perceptual system.
Visualization systems today are characterized by a rich visual complexity that arises from multiple visual channels, including color, texture and geometry. While each channel may be associated with a saliency field, previous work was solely focused on color and as such, does not consider other channels of visual appearance such as texture and geometry, or in the interactions amongst multiple channels for visual attention. How channels aggregate and the interaction between multiple channels also has not been explored. Further, with respect to geometry, a view independent method has not been presented that changes mean curvature values of vertices to change a saliency of a region.
Accordingly, it may be beneficial to obtain saliency values for a visual medium based on a plurality of visual channels, where the saliency values are based on at least one of computer-generated modeling, user-specified input and eye-tracking, aggregate the obtained saliency values, and classify regions of the visual medium based on the aggregated saliency values. In the case that one of the visual channels is geometry, it may be useful to calculate a change in mean curvature for a plurality of vertices around a region and displace the vertices in accordance with the calculated change in mean curvature to change a saliency of the region.